The value of automated follicle volume measurements in IVF/ICSI

Background/Aims: The objective of this literature study is to investigate the place of recent software technology sonography-based automated volume count (SonoAVC) for the automatic measurement of follicular volumes in IVF/ICSI. Its advantages and disadvantages and potential future developments are evaluated. Methods: A total of 74 articles were read via a PubMed literature study.The literature study included 53 articles, 32 of which for the systematic review. Results: The SonoAVC software shows excellent accuracy. Comparing the technology with the “golden standard” two-dimensional (2D) manual follicle measurements, SonoAVC leads to a significantly lower intra- and inter-observer variability. However, there is no significant difference in clinical outcome (pregnancy rate).We noted a significant advantage in the time gained, both for doctor and patient. By storing the images, the technology offers the possibility of including a quality control and continuous training and further standardization of follicular monitoring can be expected. Ovarian reserve testing by measuring the antral follicle count with SonoAVC is highly reliable. Conclusion: This overview of previously published literature shows how SonoAVC offers advantages for clinical practice, without losing any accuracy or reliability. Doctors should be motivated to the general use of follicular volumes instead of follicular diameters

Improving elevation resolution in phased-array inspections for NDT

The Phased Array Ultrasonic Technique (PAUT) offers great advantages over the conventional ultrasound technique (UT), particularly because of beam focusing, beam steering and electronic scanning capabilities. However, the 2D images obtained have usually low resolution in the direction perpendicular to the array elements, which limits the inspection quality of large components by mechanical scanning. This paper describes a novel approach to improve image quality in these situations, by combining three ultrasonic techniques: Phased Array with dynamic depth focusing in reception, Synthetic Aperture Focusing Technique (SAFT) and Phase Coherence Imaging (PCI). To be applied with conventional NDT arrays (1D and non-focused in elevation) a special mask to produce a wide beam in the movement direction was designed and analysed by simulation and experimentally. Then, the imaging algorithm is presented and validated by the inspection of test samples. The obtained images quality is comparable to that obtained with an equivalent matrix array, but using conventional NDT arrays and equipments, and implemented in real time.Fil: Brizuela, Jose David. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Camacho, J.. Consejo Superior de Investigaciones Científicas; EspañaFil: Cosarinsky, Guillermo Gerardo. Comisión Nacional de Energía Atómica; ArgentinaFil: Iriarte, Juan Manuel. Comisión Nacional de Energía Atómica; ArgentinaFil: Cruza, Jorge F.. Consejo Superior de Investigaciones Científicas; Españ

Computer-assisted access to the kidney

OBJECTIVES: The aim of this paper is to introduce the principles of computer-assisted access to the kidney. The system provides the surgeon with a pre-operative 3D planning on computed tomography (CT) images. After a rigid registration with space-localized ultrasound (US) data, preoperative planning can be transferred to the intra-operative conditions and an intuitive man-machine interface allows the user to perform a puncture. MATERIAL AND METHODS: Both CT and US images of informed normal volunteer were obtained to perform calculation on the accuracy of registration and punctures were carried out on a kidney phantom to measure the precision of the whole of the system. RESULTS: We carried out millimetric registrations on real data and guidance experiments on a kidney phantom showed encouraging results of 4.7 mm between planned and reached targets. We noticed that the most significant error was related to the needle deflection during the puncture. CONCLUSION: Preliminary results are encouraging. Further work will be undertaken to improve efficiency and accuracy, and to take breathing into account

Volumetric three-dimensional intravascular ultrasound visualization using shape-based nonlinear interpolation

BACKGROUND: Intravascular ultrasound (IVUS) is a standard imaging modality for identification of plaque formation in the coronary and peripheral arteries. Volumetric three-dimensional (3D) IVUS visualization provides a powerful tool to overcome the limited comprehensive information of 2D IVUS in terms of complex spatial distribution of arterial morphology and acoustic backscatter information. Conventional 3D IVUS techniques provide sub-optimal visualization of arterial morphology or lack acoustic information concerning arterial structure due in part to low quality of image data and the use of pixel-based IVUS image reconstruction algorithms. In the present study, we describe a novel volumetric 3D IVUS reconstruction algorithm to utilize IVUS signal data and a shape-based nonlinear interpolation. METHODS: We developed an algorithm to convert a series of IVUS signal data into a fully volumetric 3D visualization. Intermediary slices between original 2D IVUS slices were generated utilizing the natural cubic spline interpolation to consider the nonlinearity of both vascular structure geometry and acoustic backscatter in the arterial wall. We evaluated differences in image quality between the conventional pixel-based interpolation and the shape-based nonlinear interpolation methods using both virtual vascular phantom data and in vivo IVUS data of a porcine femoral artery. Volumetric 3D IVUS images of the arterial segment reconstructed using the two interpolation methods were compared. RESULTS: In vitro validation and in vivo comparative studies with the conventional pixel-based interpolation method demonstrated more robustness of the shape-based nonlinear interpolation algorithm in determining intermediary 2D IVUS slices. Our shape-based nonlinear interpolation demonstrated improved volumetric 3D visualization of the in vivo arterial structure and more realistic acoustic backscatter distribution compared to the conventional pixel-based interpolation method. CONCLUSIONS: This novel 3D IVUS visualization strategy has the potential to improve ultrasound imaging of vascular structure information, particularly atheroma determination. Improved volumetric 3D visualization with accurate acoustic backscatter information can help with ultrasound molecular imaging of atheroma component distribution

Medical image computing and computer-aided medical interventions applied to soft tissues. Work in progress in urology

Until recently, Computer-Aided Medical Interventions (CAMI) and Medical Robotics have focused on rigid and non deformable anatomical structures. Nowadays, special attention is paid to soft tissues, raising complex issues due to their mobility and deformation. Mini-invasive digestive surgery was probably one of the first fields where soft tissues were handled through the development of simulators, tracking of anatomical structures and specific assistance robots. However, other clinical domains, for instance urology, are concerned. Indeed, laparoscopic surgery, new tumour destruction techniques (e.g. HIFU, radiofrequency, or cryoablation), increasingly early detection of cancer, and use of interventional and diagnostic imaging modalities, recently opened new challenges to the urologist and scientists involved in CAMI. This resulted in the last five years in a very significant increase of research and developments of computer-aided urology systems. In this paper, we propose a description of the main problems related to computer-aided diagnostic and therapy of soft tissues and give a survey of the different types of assistance offered to the urologist: robotization, image fusion, surgical navigation. Both research projects and operational industrial systems are discussed

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201